Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy

Abstract

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.

Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling

“…This section firstly reviews the existing research from three aspects: the building model [2][3][4][5][6][7][8][9], the central air conditioning energy consumption model [10][11][12][13][14][15][16], and the application of central air conditioning in power system demand response [17][18][19][20][21][22][23][24][25]. Then, it summarizes the challenges of central air conditioning participating in power system demand response and points out the contributions made by this research.…”

“…Through the formulation of reasonable scheduling strategies, air conditioning loads can provide auxiliary services for the power system, such as frequency regulation, peak shaving, power fluctuation reduction of the tie line, fluctuation reduction of renewable energy, and maximum consumption of renewable energy [17][18][19][20][21][22][23][24][25]. Based on the secondorder ETP model, reference [19] established a load-reduction strategy with alternative start-stop of the central air conditioning clusters.…”